Data storytelling. What is it, and why does everyone want it?

Economist and political scientist Herbert A. Simon once said, “A wealth of information creates a poverty of attention.” That’s certainly true of data. In 2023 alone, we’ll generate approximately 120 zettabytes (that’s 120 trillion gigabytes) of data, most of which has incredible commercial value and potential. But we can only tap that potential if the data is used. The goal isn’t the measurement itself – not really – it’s what you do with it.  

The mass proliferation of personal data over the last decade has led to a peculiar commercial problem: we understand customer behaviour better than ever before, but the sheer amount of data being processed makes it harder and harder to extract the value. In other words, we don’t just need numbers. We need to know what they mean. 

Data storytelling is a subset of data science, and it’s quickly becoming a vital weapon in the analyst’s arsenal. Forbes just named it one of the most important tech skills for 2023. In essence, data storytelling is simply the ability to weave a narrative around complex data and analytics in order to influence action. It’s being able to find, and most importantly tell, the story behind the numbers.

The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.

- Dr. Hal R.Varian, Google’s Chief Economist (2009).

The resulting years have more or less born this out. Data, when not communicated effectively or aligned with business objectives, isn’t very useful.  

Now consider the fact that, according to research commissioned by Tableau from Forrester, by 2025 70 per cent of jobs will involve working with data, and you can begin to see why data storytelling matters so much. Having data scientists who don’t just analyse data but also synthesise its meaning will be a huge commercial advantage. In some ways, it’s the same distinction between knowledge and wisdom. One is esoteric, the other is useful.  

Data storytelling weaves a narrative around data in order to communicate meaning and extract value. That’s its purpose. But there are many other benefits. Data storytelling helps educate the wider business on the importance of data science and analysis. It brings senior stakeholders along on the journey and helps improve management buy-in. It also adds a human touch to your data, turning complex numbers into a coherent story, which can then be applied to real business problems.  

In other words, data storytelling inspires people to take action. It’s the missing link between analysis and actual change. In that way, it shares some characteristics with data visualisation. Both fields try to convey complex information: visualisation does it through charts and graphs, storytelling does it with narrative. 

It’s likely that data storytelling will be worked into existing data science degrees as a necessary skill, right up there with programming, visualisation and database management. However, such is the importance of data storytelling, some institutions, like RMIT Online, are beginning to offer dedicated data storytelling courses.  

In terms of hard skills, data storytellers use visualisation software, like Microsoft’s Power BI, to turn data into immersive, interactive insights. But you’ll also need to be able to align data sets with existing business goals, apply ethical considerations and minimise bias, and – most importantly – create a compelling narrative. 

In this way, data storytelling is a lot like traditional storytelling. You’ll have to look at your data sets, figure out what they’re telling you, then tailor that story for a particular audience. Find a data hook. Ask hypothetical questions. Back up your insights with visualisation tools. Create a linear flow from business problem through to data-driven solution. Synthesise huge data pools and pluck out the relevant information.  

Many of these aptitudes will be driven by soft skills: communication, curiosity, problem-solving, creativity, even a sense of humour. The challenge is to make analytics engaging. Because it’s that engagement that drives buy-in, budget allocation, and ultimately, action. As writer Andrew Stanton once said: “Make the audience put things together. Don't give them four, give them two plus two. The elements you provide and the order you place them in is crucial to whether you succeed or fail at engaging the audience.”  

This article was originally published on 17 August 2023.